Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory339.9 KiB
Average record size in memory696.0 B

Variable types

Text2
Numeric7
Categorical11
DateTime1

Alerts

Failed_Transaction_Count_7d is highly overall correlated with Fraud_LabelHigh correlation
Fraud_Label is highly overall correlated with Failed_Transaction_Count_7d and 1 other fieldsHigh correlation
Risk_Score is highly overall correlated with Fraud_LabelHigh correlation
IP_Address_Flag is highly imbalanced (73.1%) Imbalance
Transaction_ID has unique values Unique
Timestamp has unique values Unique
Account_Balance has unique values Unique
Transaction_Distance has unique values Unique

Reproduction

Analysis started2025-04-17 18:14:58.501810
Analysis finished2025-04-17 18:15:15.064947
Duration16.56 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

Transaction_ID
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size36.0 KiB
2025-04-17T15:15:15.389535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.76
Min length6

Characters and Unicode

Total characters4380
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowTXN_27465
2nd rowTXN_6571
3rd rowTXN_31432
4th rowTXN_21596
5th rowTXN_19097
ValueCountFrequency (%)
txn_27465 1
 
0.2%
txn_25489 1
 
0.2%
txn_31432 1
 
0.2%
txn_21596 1
 
0.2%
txn_19097 1
 
0.2%
txn_8146 1
 
0.2%
txn_26348 1
 
0.2%
txn_25338 1
 
0.2%
txn_45866 1
 
0.2%
txn_4122 1
 
0.2%
Other values (490) 490
98.0%
2025-04-17T15:15:15.993583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 500
11.4%
X 500
11.4%
N 500
11.4%
_ 500
11.4%
1 298
 
6.8%
2 289
 
6.6%
4 288
 
6.6%
3 277
 
6.3%
6 231
 
5.3%
5 225
 
5.1%
Other values (4) 772
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 500
11.4%
X 500
11.4%
N 500
11.4%
_ 500
11.4%
1 298
 
6.8%
2 289
 
6.6%
4 288
 
6.6%
3 277
 
6.3%
6 231
 
5.3%
5 225
 
5.1%
Other values (4) 772
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 500
11.4%
X 500
11.4%
N 500
11.4%
_ 500
11.4%
1 298
 
6.8%
2 289
 
6.6%
4 288
 
6.6%
3 277
 
6.3%
6 231
 
5.3%
5 225
 
5.1%
Other values (4) 772
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 500
11.4%
X 500
11.4%
N 500
11.4%
_ 500
11.4%
1 298
 
6.8%
2 289
 
6.6%
4 288
 
6.6%
3 277
 
6.3%
6 231
 
5.3%
5 225
 
5.1%
Other values (4) 772
17.6%
Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
2025-04-17T15:15:16.380167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4500
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)93.6%

Sample

1st rowUSER_2904
2nd rowUSER_2108
3rd rowUSER_4686
4th rowUSER_8562
5th rowUSER_6120
ValueCountFrequency (%)
user_8446 2
 
0.4%
user_6980 2
 
0.4%
user_2602 2
 
0.4%
user_5200 2
 
0.4%
user_6798 2
 
0.4%
user_4929 2
 
0.4%
user_3935 2
 
0.4%
user_4771 2
 
0.4%
user_3963 2
 
0.4%
user_2719 2
 
0.4%
Other values (474) 480
96.0%
2025-04-17T15:15:16.988342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 500
11.1%
S 500
11.1%
E 500
11.1%
R 500
11.1%
_ 500
11.1%
4 219
 
4.9%
5 209
 
4.6%
7 209
 
4.6%
6 208
 
4.6%
3 207
 
4.6%
Other values (5) 948
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 500
11.1%
S 500
11.1%
E 500
11.1%
R 500
11.1%
_ 500
11.1%
4 219
 
4.9%
5 209
 
4.6%
7 209
 
4.6%
6 208
 
4.6%
3 207
 
4.6%
Other values (5) 948
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 500
11.1%
S 500
11.1%
E 500
11.1%
R 500
11.1%
_ 500
11.1%
4 219
 
4.9%
5 209
 
4.6%
7 209
 
4.6%
6 208
 
4.6%
3 207
 
4.6%
Other values (5) 948
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 500
11.1%
S 500
11.1%
E 500
11.1%
R 500
11.1%
_ 500
11.1%
4 219
 
4.9%
5 209
 
4.6%
7 209
 
4.6%
6 208
 
4.6%
3 207
 
4.6%
Other values (5) 948
21.1%

Transaction_Amount
Real number (ℝ)

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.97278
Minimum0.09
Maximum539.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:17.225237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile5.382
Q133.04
median70.455
Q3131.5625
95-th percentile271.985
Maximum539.55
Range539.46
Interquartile range (IQR)98.5225

Descriptive statistics

Standard deviation86.690546
Coefficient of variation (CV)0.90328264
Kurtosis3.6225802
Mean95.97278
Median Absolute Deviation (MAD)45.875
Skewness1.6630771
Sum47986.39
Variance7515.2508
MonotonicityNot monotonic
2025-04-17T15:15:17.480475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.32 2
 
0.4%
63.49 2
 
0.4%
53.94 2
 
0.4%
11.17 2
 
0.4%
0.91 2
 
0.4%
207.55 1
 
0.2%
44.51 1
 
0.2%
48.17 1
 
0.2%
86.78 1
 
0.2%
37.99 1
 
0.2%
Other values (485) 485
97.0%
ValueCountFrequency (%)
0.09 1
0.2%
0.45 1
0.2%
0.5 1
0.2%
0.6 1
0.2%
0.76 1
0.2%
0.83 1
0.2%
0.88 1
0.2%
0.91 2
0.4%
1.19 1
0.2%
1.7 1
0.2%
ValueCountFrequency (%)
539.55 1
0.2%
511.46 1
0.2%
501.72 1
0.2%
390.57 1
0.2%
384.28 1
0.2%
366.4 1
0.2%
354.71 1
0.2%
349.99 1
0.2%
346.96 1
0.2%
344.5 1
0.2%

Transaction_Type
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.0 KiB
POS
133 
Online
132 
ATM Withdrawal
118 
Bank Transfer
117 

Length

Max length14
Median length13
Mean length8.728
Min length3

Characters and Unicode

Total characters4364
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS
2nd rowPOS
3rd rowPOS
4th rowATM Withdrawal
5th rowOnline

Common Values

ValueCountFrequency (%)
POS 133
26.6%
Online 132
26.4%
ATM Withdrawal 118
23.6%
Bank Transfer 117
23.4%

Length

2025-04-17T15:15:17.705705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:17.914014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pos 133
18.1%
online 132
18.0%
atm 118
16.1%
withdrawal 118
16.1%
bank 117
15.9%
transfer 117
15.9%

Most occurring characters

ValueCountFrequency (%)
n 498
 
11.4%
a 470
 
10.8%
r 352
 
8.1%
O 265
 
6.1%
l 250
 
5.7%
i 250
 
5.7%
e 249
 
5.7%
T 235
 
5.4%
235
 
5.4%
P 133
 
3.0%
Other values (12) 1427
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 498
 
11.4%
a 470
 
10.8%
r 352
 
8.1%
O 265
 
6.1%
l 250
 
5.7%
i 250
 
5.7%
e 249
 
5.7%
T 235
 
5.4%
235
 
5.4%
P 133
 
3.0%
Other values (12) 1427
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 498
 
11.4%
a 470
 
10.8%
r 352
 
8.1%
O 265
 
6.1%
l 250
 
5.7%
i 250
 
5.7%
e 249
 
5.7%
T 235
 
5.4%
235
 
5.4%
P 133
 
3.0%
Other values (12) 1427
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 498
 
11.4%
a 470
 
10.8%
r 352
 
8.1%
O 265
 
6.1%
l 250
 
5.7%
i 250
 
5.7%
e 249
 
5.7%
T 235
 
5.4%
235
 
5.4%
P 133
 
3.0%
Other values (12) 1427
32.7%

Timestamp
Date

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Minimum2023-01-01 06:19:00
Maximum2023-12-31 16:09:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-17T15:15:18.202268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:18.475065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Account_Balance
Real number (ℝ)

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51389.174
Minimum650.47
Maximum99951.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:18.763798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum650.47
5-th percentile4991.5565
Q125981.11
median53229.025
Q377041.788
95-th percentile95072.091
Maximum99951.07
Range99300.6
Interquartile range (IQR)51060.678

Descriptive statistics

Standard deviation29164.577
Coefficient of variation (CV)0.56752376
Kurtosis-1.1962902
Mean51389.174
Median Absolute Deviation (MAD)25257.285
Skewness-0.10407462
Sum25694587
Variance8.5057257 × 108
MonotonicityNot monotonic
2025-04-17T15:15:19.029486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1941.7 1
 
0.2%
91157.79 1
 
0.2%
52790.73 1
 
0.2%
3705.52 1
 
0.2%
22614.14 1
 
0.2%
913.67 1
 
0.2%
58801.25 1
 
0.2%
81776.83 1
 
0.2%
85405.37 1
 
0.2%
7826.32 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
650.47 1
0.2%
753.87 1
0.2%
913.67 1
0.2%
917.46 1
0.2%
1167.54 1
0.2%
1301.09 1
0.2%
1590.3 1
0.2%
1647.56 1
0.2%
1724.22 1
0.2%
1941.7 1
0.2%
ValueCountFrequency (%)
99951.07 1
0.2%
99771.26 1
0.2%
99700.56 1
0.2%
99511.66 1
0.2%
99437.09 1
0.2%
99350.52 1
0.2%
99169.01 1
0.2%
99068.59 1
0.2%
98487.45 1
0.2%
98162.68 1
0.2%

Device_Type
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Tablet
182 
Mobile
164 
Laptop
154 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop
2nd rowMobile
3rd rowTablet
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Tablet 182
36.4%
Mobile 164
32.8%
Laptop 154
30.8%

Length

2025-04-17T15:15:19.265879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:19.447358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
tablet 182
36.4%
mobile 164
32.8%
laptop 154
30.8%

Most occurring characters

ValueCountFrequency (%)
b 346
11.5%
l 346
11.5%
e 346
11.5%
a 336
11.2%
t 336
11.2%
o 318
10.6%
p 308
10.3%
T 182
6.1%
M 164
5.5%
i 164
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 346
11.5%
l 346
11.5%
e 346
11.5%
a 336
11.2%
t 336
11.2%
o 318
10.6%
p 308
10.3%
T 182
6.1%
M 164
5.5%
i 164
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 346
11.5%
l 346
11.5%
e 346
11.5%
a 336
11.2%
t 336
11.2%
o 318
10.6%
p 308
10.3%
T 182
6.1%
M 164
5.5%
i 164
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 346
11.5%
l 346
11.5%
e 346
11.5%
a 336
11.2%
t 336
11.2%
o 318
10.6%
p 308
10.3%
T 182
6.1%
M 164
5.5%
i 164
5.5%

Location
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size34.8 KiB
London
105 
Tokyo
104 
Sydney
99 
New York
97 
Mumbai
95 

Length

Max length8
Median length6
Mean length6.18
Min length5

Characters and Unicode

Total characters3090
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTokyo
2nd rowLondon
3rd rowNew York
4th rowTokyo
5th rowMumbai

Common Values

ValueCountFrequency (%)
London 105
21.0%
Tokyo 104
20.8%
Sydney 99
19.8%
New York 97
19.4%
Mumbai 95
19.0%

Length

2025-04-17T15:15:19.674659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:19.876305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
london 105
17.6%
tokyo 104
17.4%
sydney 99
16.6%
new 97
16.2%
york 97
16.2%
mumbai 95
15.9%

Most occurring characters

ValueCountFrequency (%)
o 515
16.7%
n 309
 
10.0%
y 302
 
9.8%
d 204
 
6.6%
k 201
 
6.5%
e 196
 
6.3%
L 105
 
3.4%
T 104
 
3.4%
S 99
 
3.2%
Y 97
 
3.1%
Other values (10) 958
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 515
16.7%
n 309
 
10.0%
y 302
 
9.8%
d 204
 
6.6%
k 201
 
6.5%
e 196
 
6.3%
L 105
 
3.4%
T 104
 
3.4%
S 99
 
3.2%
Y 97
 
3.1%
Other values (10) 958
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 515
16.7%
n 309
 
10.0%
y 302
 
9.8%
d 204
 
6.6%
k 201
 
6.5%
e 196
 
6.3%
L 105
 
3.4%
T 104
 
3.4%
S 99
 
3.2%
Y 97
 
3.1%
Other values (10) 958
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 515
16.7%
n 309
 
10.0%
y 302
 
9.8%
d 204
 
6.6%
k 201
 
6.5%
e 196
 
6.3%
L 105
 
3.4%
T 104
 
3.4%
S 99
 
3.2%
Y 97
 
3.1%
Other values (10) 958
31.0%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
Electronics
117 
Groceries
111 
Restaurants
100 
Travel
88 
Clothing
84 

Length

Max length11
Median length9
Mean length9.172
Min length6

Characters and Unicode

Total characters4586
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronics
2nd rowGroceries
3rd rowGroceries
4th rowElectronics
5th rowGroceries

Common Values

ValueCountFrequency (%)
Electronics 117
23.4%
Groceries 111
22.2%
Restaurants 100
20.0%
Travel 88
17.6%
Clothing 84
16.8%

Length

2025-04-17T15:15:20.126039image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:20.323555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
electronics 117
23.4%
groceries 111
22.2%
restaurants 100
20.0%
travel 88
17.6%
clothing 84
16.8%

Most occurring characters

ValueCountFrequency (%)
e 527
11.5%
r 527
11.5%
s 428
9.3%
t 401
8.7%
c 345
7.5%
o 312
 
6.8%
i 312
 
6.8%
n 301
 
6.6%
l 289
 
6.3%
a 288
 
6.3%
Other values (9) 856
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 527
11.5%
r 527
11.5%
s 428
9.3%
t 401
8.7%
c 345
7.5%
o 312
 
6.8%
i 312
 
6.8%
n 301
 
6.6%
l 289
 
6.3%
a 288
 
6.3%
Other values (9) 856
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 527
11.5%
r 527
11.5%
s 428
9.3%
t 401
8.7%
c 345
7.5%
o 312
 
6.8%
i 312
 
6.8%
n 301
 
6.6%
l 289
 
6.3%
a 288
 
6.3%
Other values (9) 856
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 527
11.5%
r 527
11.5%
s 428
9.3%
t 401
8.7%
c 345
7.5%
o 312
 
6.8%
i 312
 
6.8%
n 301
 
6.6%
l 289
 
6.3%
a 288
 
6.3%
Other values (9) 856
18.7%

IP_Address_Flag
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
0
477 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%

Length

2025-04-17T15:15:20.558219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:20.731098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 477
95.4%
1 23
 
4.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
0
436 
1
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Length

2025-04-17T15:15:20.922217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:21.104639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Daily_Transaction_Count
Real number (ℝ)

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.572
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:21.278029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0220853
Coefficient of variation (CV)0.53117873
Kurtosis-1.2012453
Mean7.572
Median Absolute Deviation (MAD)3
Skewness-0.053739393
Sum3786
Variance16.17717
MonotonicityNot monotonic
2025-04-17T15:15:21.487178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 48
 
9.6%
11 42
 
8.4%
4 39
 
7.8%
8 38
 
7.6%
13 38
 
7.6%
2 36
 
7.2%
1 36
 
7.2%
7 35
 
7.0%
14 35
 
7.0%
6 33
 
6.6%
Other values (4) 120
24.0%
ValueCountFrequency (%)
1 36
7.2%
2 36
7.2%
3 32
6.4%
4 39
7.8%
5 30
6.0%
6 33
6.6%
7 35
7.0%
8 38
7.6%
9 31
6.2%
10 48
9.6%
ValueCountFrequency (%)
14 35
7.0%
13 38
7.6%
12 27
5.4%
11 42
8.4%
10 48
9.6%
9 31
6.2%
8 38
7.6%
7 35
7.0%
6 33
6.6%
5 30
6.0%

Avg_Transaction_Amount_7d
Real number (ℝ)

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.1637
Minimum12.32
Maximum499.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:21.741267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum12.32
5-th percentile32.2135
Q1125.2725
median244.335
Q3386.8425
95-th percentile476.9035
Maximum499.66
Range487.34
Interquartile range (IQR)261.57

Descriptive statistics

Standard deviation146.16773
Coefficient of variation (CV)0.5773645
Kurtosis-1.2379057
Mean253.1637
Median Absolute Deviation (MAD)130.895
Skewness0.025452626
Sum126581.85
Variance21365.006
MonotonicityNot monotonic
2025-04-17T15:15:22.002387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405.37 2
 
0.4%
225.02 2
 
0.4%
74.85 2
 
0.4%
154.83 1
 
0.2%
489.48 1
 
0.2%
130.72 1
 
0.2%
61.45 1
 
0.2%
348.53 1
 
0.2%
162.59 1
 
0.2%
128.84 1
 
0.2%
Other values (487) 487
97.4%
ValueCountFrequency (%)
12.32 1
0.2%
12.69 1
0.2%
14.93 1
0.2%
15.09 1
0.2%
15.11 1
0.2%
15.21 1
0.2%
15.64 1
0.2%
16.24 1
0.2%
17.49 1
0.2%
18.33 1
0.2%
ValueCountFrequency (%)
499.66 1
0.2%
499.18 1
0.2%
498.74 1
0.2%
498.3 1
0.2%
497.28 1
0.2%
496.83 1
0.2%
495.34 1
0.2%
494.78 1
0.2%
494.46 1
0.2%
491.76 1
0.2%

Failed_Transaction_Count_7d
Categorical

High correlation 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
3
120 
1
103 
4
97 
2
91 
0
89 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Length

2025-04-17T15:15:22.511070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:22.690584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Most occurring characters

ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 120
24.0%
1 103
20.6%
4 97
19.4%
2 91
18.2%
0 89
17.8%

Card_Type
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.9 KiB
Visa
138 
Mastercard
133 
Amex
120 
Discover
109 

Length

Max length10
Median length4
Mean length6.468
Min length4

Characters and Unicode

Total characters3234
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMastercard
2nd rowMastercard
3rd rowDiscover
4th rowMastercard
5th rowMastercard

Common Values

ValueCountFrequency (%)
Visa 138
27.6%
Mastercard 133
26.6%
Amex 120
24.0%
Discover 109
21.8%

Length

2025-04-17T15:15:23.350529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:23.843401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
visa 138
27.6%
mastercard 133
26.6%
amex 120
24.0%
discover 109
21.8%

Most occurring characters

ValueCountFrequency (%)
a 404
12.5%
s 380
11.8%
r 375
11.6%
e 362
11.2%
i 247
 
7.6%
c 242
 
7.5%
V 138
 
4.3%
M 133
 
4.1%
t 133
 
4.1%
d 133
 
4.1%
Other values (6) 687
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 404
12.5%
s 380
11.8%
r 375
11.6%
e 362
11.2%
i 247
 
7.6%
c 242
 
7.5%
V 138
 
4.3%
M 133
 
4.1%
t 133
 
4.1%
d 133
 
4.1%
Other values (6) 687
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 404
12.5%
s 380
11.8%
r 375
11.6%
e 362
11.2%
i 247
 
7.6%
c 242
 
7.5%
V 138
 
4.3%
M 133
 
4.1%
t 133
 
4.1%
d 133
 
4.1%
Other values (6) 687
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 404
12.5%
s 380
11.8%
r 375
11.6%
e 362
11.2%
i 247
 
7.6%
c 242
 
7.5%
V 138
 
4.3%
M 133
 
4.1%
t 133
 
4.1%
d 133
 
4.1%
Other values (6) 687
21.2%

Card_Age
Real number (ℝ)

Distinct208
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.49
Minimum1
Maximum239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:24.094875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q164
median127
Q3186
95-th percentile230
Maximum239
Range238
Interquartile range (IQR)122

Descriptive statistics

Standard deviation70.148268
Coefficient of variation (CV)0.56348516
Kurtosis-1.1929625
Mean124.49
Median Absolute Deviation (MAD)60.5
Skewness-0.086445362
Sum62245
Variance4920.7795
MonotonicityNot monotonic
2025-04-17T15:15:24.413855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153 7
 
1.4%
168 6
 
1.2%
127 6
 
1.2%
2 6
 
1.2%
221 6
 
1.2%
223 5
 
1.0%
212 5
 
1.0%
57 5
 
1.0%
230 5
 
1.0%
72 5
 
1.0%
Other values (198) 444
88.8%
ValueCountFrequency (%)
1 2
 
0.4%
2 6
1.2%
3 3
0.6%
4 1
 
0.2%
5 3
0.6%
6 1
 
0.2%
8 2
 
0.4%
9 2
 
0.4%
10 1
 
0.2%
11 2
 
0.4%
ValueCountFrequency (%)
239 1
 
0.2%
238 1
 
0.2%
237 3
0.6%
235 5
1.0%
234 1
 
0.2%
233 4
0.8%
232 3
0.6%
231 4
0.8%
230 5
1.0%
229 3
0.6%

Transaction_Distance
Real number (ℝ)

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2459.7137
Minimum3.73
Maximum4991.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:24.716808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile194.189
Q11153.5025
median2470.365
Q33711.755
95-th percentile4707.93
Maximum4991.92
Range4988.19
Interquartile range (IQR)2558.2525

Descriptive statistics

Standard deviation1471.7772
Coefficient of variation (CV)0.59835306
Kurtosis-1.2375431
Mean2459.7137
Median Absolute Deviation (MAD)1272.435
Skewness0.0073574052
Sum1229856.8
Variance2166128.1
MonotonicityNot monotonic
2025-04-17T15:15:25.020400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3927.67 1
 
0.2%
4190.19 1
 
0.2%
3266.42 1
 
0.2%
4520.07 1
 
0.2%
2539.06 1
 
0.2%
1358.03 1
 
0.2%
121.41 1
 
0.2%
536.17 1
 
0.2%
3.73 1
 
0.2%
2985.92 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
3.73 1
0.2%
10.76 1
0.2%
15.15 1
0.2%
24.74 1
0.2%
32.97 1
0.2%
49.46 1
0.2%
55.09 1
0.2%
56.56 1
0.2%
62.48 1
0.2%
67.34 1
0.2%
ValueCountFrequency (%)
4991.92 1
0.2%
4988.42 1
0.2%
4987.02 1
0.2%
4974.04 1
0.2%
4973.12 1
0.2%
4968.94 1
0.2%
4958.55 1
0.2%
4956.08 1
0.2%
4955.02 1
0.2%
4906.54 1
0.2%
Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
OTP
130 
Password
124 
Biometric
124 
PIN
122 

Length

Max length9
Median length3
Mean length5.728
Min length3

Characters and Unicode

Total characters2864
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPassword
2nd rowPIN
3rd rowPIN
4th rowOTP
5th rowPassword

Common Values

ValueCountFrequency (%)
OTP 130
26.0%
Password 124
24.8%
Biometric 124
24.8%
PIN 122
24.4%

Length

2025-04-17T15:15:25.269468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:25.455658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
otp 130
26.0%
password 124
24.8%
biometric 124
24.8%
pin 122
24.4%

Most occurring characters

ValueCountFrequency (%)
P 376
13.1%
s 248
 
8.7%
o 248
 
8.7%
r 248
 
8.7%
i 248
 
8.7%
O 130
 
4.5%
T 130
 
4.5%
m 124
 
4.3%
c 124
 
4.3%
t 124
 
4.3%
Other values (7) 864
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 376
13.1%
s 248
 
8.7%
o 248
 
8.7%
r 248
 
8.7%
i 248
 
8.7%
O 130
 
4.5%
T 130
 
4.5%
m 124
 
4.3%
c 124
 
4.3%
t 124
 
4.3%
Other values (7) 864
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 376
13.1%
s 248
 
8.7%
o 248
 
8.7%
r 248
 
8.7%
i 248
 
8.7%
O 130
 
4.5%
T 130
 
4.5%
m 124
 
4.3%
c 124
 
4.3%
t 124
 
4.3%
Other values (7) 864
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 376
13.1%
s 248
 
8.7%
o 248
 
8.7%
r 248
 
8.7%
i 248
 
8.7%
O 130
 
4.5%
T 130
 
4.5%
m 124
 
4.3%
c 124
 
4.3%
t 124
 
4.3%
Other values (7) 864
30.2%

Risk_Score
Real number (ℝ)

High correlation 

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5071742
Minimum0.0055
Maximum0.9963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2025-04-17T15:15:25.721161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0055
5-th percentile0.048385
Q10.226625
median0.51435
Q30.799125
95-th percentile0.9648
Maximum0.9963
Range0.9908
Interquartile range (IQR)0.5725

Descriptive statistics

Standard deviation0.30667753
Coefficient of variation (CV)0.60467889
Kurtosis-1.3641379
Mean0.5071742
Median Absolute Deviation (MAD)0.2883
Skewness0.0055813784
Sum253.5871
Variance0.09405111
MonotonicityNot monotonic
2025-04-17T15:15:25.988609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1062 2
 
0.4%
0.8142 2
 
0.4%
0.0969 2
 
0.4%
0.472 2
 
0.4%
0.4979 2
 
0.4%
0.8997 2
 
0.4%
0.1972 2
 
0.4%
0.0435 2
 
0.4%
0.8573 2
 
0.4%
0.7297 1
 
0.2%
Other values (481) 481
96.2%
ValueCountFrequency (%)
0.0055 1
0.2%
0.0101 1
0.2%
0.014 1
0.2%
0.0145 1
0.2%
0.0153 1
0.2%
0.0185 1
0.2%
0.0196 1
0.2%
0.0236 1
0.2%
0.0261 1
0.2%
0.0266 1
0.2%
ValueCountFrequency (%)
0.9963 1
0.2%
0.9949 1
0.2%
0.9922 1
0.2%
0.9916 1
0.2%
0.9912 1
0.2%
0.991 1
0.2%
0.988 1
0.2%
0.9878 1
0.2%
0.9871 1
0.2%
0.9853 1
0.2%

Is_Weekend
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
0
344 
1
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Length

2025-04-17T15:15:26.249265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:26.420191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Most occurring characters

ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 344
68.8%
1 156
31.2%

Fraud_Label
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
0
321 
1
179 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Length

2025-04-17T15:15:26.611290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T15:15:26.780767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Most occurring characters

ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 321
64.2%
1 179
35.8%

Interactions

2025-04-17T15:15:11.590561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:14:59.920942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:01.159918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:02.408343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:03.922559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:05.710779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:08.290906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:11.876725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.092106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:01.341108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:02.612687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:04.132149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:05.901132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:09.064727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:12.190271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.274057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:01.519547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:02.829987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:04.685889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:06.083651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:09.614053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:12.596009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.454639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:01.700134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:03.049155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:04.898375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:06.265679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:10.076323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:12.933284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.631601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:01.877381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:03.282852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:05.105409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:06.453752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:10.784416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:13.347082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.804569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:02.049990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:03.496607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:05.302031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:06.819246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:11.025860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:13.761179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:00.983136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:02.225927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:03.705870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:05.516871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:07.678058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-17T15:15:11.309197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-04-17T15:15:26.931738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Account_BalanceAuthentication_MethodAvg_Transaction_Amount_7dCard_AgeCard_TypeDaily_Transaction_CountDevice_TypeFailed_Transaction_Count_7dFraud_LabelIP_Address_FlagIs_WeekendLocationMerchant_CategoryPrevious_Fraudulent_ActivityRisk_ScoreTransaction_AmountTransaction_DistanceTransaction_Type
Account_Balance1.0000.084-0.0050.0640.000-0.0100.0000.0000.0000.0000.0000.0000.0890.025-0.0320.0020.0020.000
Authentication_Method0.0841.0000.0000.0000.0000.0630.0000.0430.0500.0570.0000.0000.0000.0720.0000.0610.0000.060
Avg_Transaction_Amount_7d-0.0050.0001.0000.0100.0000.0800.0390.0000.0000.0000.0000.0270.0000.000-0.0170.044-0.0210.000
Card_Age0.0640.0000.0101.0000.0000.0200.0990.0000.1240.0380.0490.0000.0000.0000.0580.025-0.0150.098
Card_Type0.0000.0000.0000.0001.0000.0180.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.000
Daily_Transaction_Count-0.0100.0630.0800.0200.0181.0000.0000.0360.0320.0000.1370.0400.0000.133-0.011-0.019-0.0530.038
Device_Type0.0000.0000.0390.0990.0000.0001.0000.0000.0000.0000.0000.0000.0000.0670.0000.0650.0000.000
Failed_Transaction_Count_7d0.0000.0430.0000.0000.0000.0360.0001.0000.6540.0000.1020.0000.0760.1070.0290.0000.0610.080
Fraud_Label0.0000.0500.0000.1240.0000.0320.0000.6541.0000.0000.0000.0000.0000.0840.6340.0000.0740.000
IP_Address_Flag0.0000.0570.0000.0380.0000.0000.0000.0000.0001.0000.0000.0300.0000.0000.0990.0780.0000.030
Is_Weekend0.0000.0000.0000.0490.0000.1370.0000.1020.0000.0001.0000.0000.0000.0380.0000.1170.0760.000
Location0.0000.0000.0270.0000.0000.0400.0000.0000.0000.0300.0001.0000.0280.0000.0000.0000.0000.036
Merchant_Category0.0890.0000.0000.0000.0210.0000.0000.0760.0000.0000.0000.0281.0000.0000.0000.0000.0980.000
Previous_Fraudulent_Activity0.0250.0720.0000.0000.0000.1330.0670.1070.0840.0000.0380.0000.0001.0000.0000.0000.0000.000
Risk_Score-0.0320.000-0.0170.0580.000-0.0110.0000.0290.6340.0990.0000.0000.0000.0001.000-0.070-0.0110.000
Transaction_Amount0.0020.0610.0440.0250.000-0.0190.0650.0000.0000.0780.1170.0000.0000.000-0.0701.0000.0090.068
Transaction_Distance0.0020.000-0.021-0.0150.000-0.0530.0000.0610.0740.0000.0760.0000.0980.000-0.0110.0091.0000.064
Transaction_Type0.0000.0600.0000.0980.0000.0380.0000.0800.0000.0300.0000.0360.0000.0000.0000.0680.0641.000

Missing values

2025-04-17T15:15:14.137762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-17T15:15:14.783769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction_IDUser_IDTransaction_AmountTransaction_TypeTimestampAccount_BalanceDevice_TypeLocationMerchant_CategoryIP_Address_FlagPrevious_Fraudulent_ActivityDaily_Transaction_CountAvg_Transaction_Amount_7dFailed_Transaction_Count_7dCard_TypeCard_AgeTransaction_DistanceAuthentication_MethodRisk_ScoreIs_WeekendFraud_Label
20515TXN_27465USER_29042.21POS2023-12-19 18:41:001941.70LaptopTokyoElectronics008154.832Mastercard833927.67Password0.612300
42486TXN_6571USER_2108136.05POS2023-06-02 23:29:008476.80MobileLondonGroceries00718.330Mastercard39409.85PIN0.675500
31440TXN_31432USER_4686127.70POS2023-11-20 15:41:0087065.25TabletNew YorkGroceries0010404.154Discover1564607.25PIN0.966701
41116TXN_21596USER_8562105.77ATM Withdrawal2023-01-18 08:38:003526.98MobileTokyoElectronics0111145.094Mastercard632984.05OTP0.187701
48739TXN_19097USER_6120344.50Online2023-03-12 23:54:0077426.86MobileMumbaiGroceries001314.933Mastercard172138.69Password0.562000
19808TXN_8146USER_2046366.40ATM Withdrawal2023-08-06 02:45:0073136.19MobileLondonTravel005499.183Mastercard1204184.70PIN0.753800
5765TXN_26348USER_108933.01Online2023-07-29 16:38:0049531.66TabletNew YorkElectronics001017.492Mastercard643851.88PIN0.876201
48222TXN_25338USER_355638.16ATM Withdrawal2023-02-21 19:22:0073354.76TabletTokyoRestaurants1013398.651Visa1824423.11OTP0.844400
18150TXN_45866USER_4622162.18Online2023-12-30 23:14:0068531.93LaptopTokyoTravel00421.720Mastercard323525.54PIN0.085700
26845TXN_4122USER_298763.18ATM Withdrawal2023-01-28 20:56:004443.37MobileLondonRestaurants007268.312Discover91136.18OTP0.814210
Transaction_IDUser_IDTransaction_AmountTransaction_TypeTimestampAccount_BalanceDevice_TypeLocationMerchant_CategoryIP_Address_FlagPrevious_Fraudulent_ActivityDaily_Transaction_CountAvg_Transaction_Amount_7dFailed_Transaction_Count_7dCard_TypeCard_AgeTransaction_DistanceAuthentication_MethodRisk_ScoreIs_WeekendFraud_Label
26484TXN_21129USER_449981.52Online2023-05-18 07:41:0058899.02TabletSydneyClothing003419.250Amex1711065.51PIN0.082500
7089TXN_48917USER_8243539.55ATM Withdrawal2023-01-29 19:45:006764.52TabletTokyoClothing016165.764Mastercard2054987.02PIN0.590501
12054TXN_19711USER_412150.36POS2023-04-01 10:16:0066612.73LaptopMumbaiRestaurants0010169.023Amex571808.35PIN0.055700
16918TXN_4620USER_290751.19Bank Transfer2023-07-29 20:03:0030145.43MobileMumbaiClothing007241.594Visa84378.91OTP0.282501
37194TXN_40686USER_3567116.64POS2023-01-03 13:52:0018692.34TabletSydneyClothing002217.132Mastercard282736.17PIN0.714700
10264TXN_23597USER_1899115.10ATM Withdrawal2023-09-24 15:44:0063724.09LaptopNew YorkRestaurants119215.452Mastercard23092.31Password0.531110
10344TXN_24013USER_790626.45Online2023-04-18 03:27:0058453.67LaptopMumbaiRestaurants002292.681Amex2161974.38PIN0.283700
20TXN_6113USER_556622.02ATM Withdrawal2023-12-28 04:45:0055851.38MobileSydneyElectronics0013179.733Visa204633.99PIN0.130510
284TXN_31520USER_9541207.98ATM Withdrawal2023-03-14 11:13:0062836.43TabletNew YorkGroceries008464.164Visa674583.53Biometric0.170611
43306TXN_48312USER_206237.40ATM Withdrawal2023-04-09 18:55:008721.46TabletSydneyGroceries0010239.230Amex1733157.88Password0.985311